Reasoning Distillation

Reasoning distillation focuses on transferring the complex reasoning abilities of large language models (LLMs) to smaller, more resource-efficient models. Current research explores techniques like knowledge distillation, leveraging chain-of-thought reasoning, and incorporating both positive and negative examples to improve the accuracy and interpretability of smaller models across various tasks, including essay scoring, planning, and scientific text generation. This work is significant because it addresses the limitations of deploying large, computationally expensive LLMs, enabling the application of advanced reasoning capabilities in resource-constrained environments and improving the explainability of model outputs.